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@article{180483,
        author = {Prof. Shah Saloni Niranjan and Mr. Pawar R.B. and Ms. Raut A.S. and Ms. Jadhav S.N.},
        title = {Diagnosing Respiratory Conditions Via Lung Sounds using CNN-LSTM},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {1337-1341},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180483},
        abstract = {Respiratory diseases rank among the 
foremost causes of mortality globally. While traditional 
lung auscultation is effective, it is hindered by 
limitations such as interference from background noise 
and reliance on the expertise of healthcare 
professionals. Recently, machine learning has emerged 
as a promising approach for the automated analysis of 
lung sounds, enhancing diagnostic accuracy and 
reducing the time required for diagnosis. This study is 
dedicated to the development of an automated system 
for lung sound classification, utilizing GTCC-based 
features in conjunction with a Multi-Layer Perceptron 
(MLP) classifier. Our system, trained on a 
comprehensive dataset comprising over 6,800 audio 
clips, achieved an impressive classification accuracy of 
99.22%, underscoring its potential to facilitate the 
early detection of respiratory diseases.},
        keywords = {Machine Learning, Lung Sound Analysis,  GTCC Features, Deep Learning, Respiratory Diseases.},
        month = {June},
        }